Changing Role of Academic Libraries in the E-learning Environment: Issues and Challenges

The development of computer and network technology is changing the education scenario and transforming the teaching and learning process from the traditional physical environment to the digital environment. It is now possible to access vast amount of information online and enable one to one communication without the confines of place or time. While E-learning and teaching is unlikely to replace face-to-face training and education it is becoming an additional delivery method, providing new learning opportunities to many users. It is also causing an impact on library services as the increased use of ICT and web based learning technologies have paved the way for providing new ICT based services and resources to the users. Online learning has a crucial role in user education, information literacy programmes and in training the library professionals. It can help students become active learners, and libraries will have to play a greater role in this process of transformation. The significance of libraries within an institution has improved due to the fact that academic libraries and information services are now responsible for e-learning within their organization.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images

Learning achievements of farmers during the transition to market-oriented organic agriculture in rural Uganda

Organic agriculture requires farmers with the ability to develop profitable agro-enterprises on their own. By drawing on four years of experiences with the Enabling Rural Innovation approach in Uganda, we outline how smallholder farmers transition to organic agriculture and, at the same time, increase their entrepreneurial skills and competences through learning. In order to document this learning we operationalised the Kirkpatrick learning evaluation model, which subsequently informed the collection of qualitative data in two study sites. Our analysis suggests that the Enabling Rural Innovation approach helps farmers to develop essential capabilities for identifying organic markets and new organic commodities, for testing these organic commodities under varying organic farm management scenarios, and for negotiating contracts with organic traders. We also observed several obstacles that confront farmers’ transition to organic agriculture when using the Enabling Rural Innovation approach. These include the long duration of agronomic experimentation and seed multiplication, expensive organic certification procedures and the absence of adequate mechanism for farmers to access crop finance services. Despite prevailing obstacles we conclude that the Enabling Rural Innovation approach provides a starting point for farmers to develop entrepreneurial competences and profitable agro-enterprises on their own.

Identification of Problem-based learning components and their effects on graduates' competencies

This study investigated the relationship between higher education and the requirement of the world of work with an emphasis on the effect of problem-based learning (PBL) on graduates' competencies. The implementation of full PBL method is costly (Albanese & Mitchell, 1993; Berkson, 1993; Finucane, Shannon, & McGrath, 2009). However, the implementation of PBL in a less than curriculum-wide mode is more achievable in a broader context (Albanese, 2000). This means higher education institutions implement only a few PBL components in the curriculum. Or a teacher implements a few PBL components at the courses level. For this kind of implementation there is a need to identify PBL components and their effects on particular educational outputs (Hmelo-Silver, 2004; Newman, 2003). So far, however there has been little research about this topic. The main aims of this study were: (1) to identify each of PBL components which were manifested in the development of a valid and reliable PBL implementation questionnaire and (2) to determine the effect of each identified PBL component to specific graduates' competencies. The analysis was based on quantitative data collected in the survey of medicine graduates of Gadjah Mada University, Indonesia. A total of 225 graduates responded to the survey. The result of confirmatory factor analysis (CFA) showed that all individual constructs of PBL and graduates' competencies had acceptable GOFs (Goodness-of-fit). Additionally, the values of the factor loadings (standardize loading estimates), the AVEs (average variance extracted), CRs (construct reliability), and ASVs (average shared squared variance) showed the proof of convergent and discriminant validity. All values indicated valid and reliable measurements. The investigation of the effects of PBL showed that each PBL component had specific effects on graduates' competencies. Interpersonal competencies were affected by Student-centred learning (β = .137; p < .05) and Small group components (β = .078; p < .05). Problem as stimulus affected Leadership (β = .182; p < .01). Real-world problems affected Personal and organisational competencies (β = .140; p < .01) and Interpersonal competencies (β = .114; p < .05). Teacher as facilitator affected Leadership (β = 142; p < .05). Self-directed learning affected Field-related competencies (β = .080; p < .05). These results can help higher education institution and educator to have informed choice about the implementation of PBL components. With this information higher education institutions and educators could fulfil their educational goals and in the same time meet their limited resources. This study seeks to improve prior studies' research method in four major ways: (1) by indentifying PBL components based on theory and empirical data; (2) by using latent variables in the structural equation modelling instead of using a variable as a proxy of a construct; (3) by using CFA to validate the latent structure of the measurement, thus providing better evidence of validity; and (4) by using graduate survey data which is suitable for analysing PBL effects in the frame work of the relationship between higher education and the world of work.

Double burden malnutrition of preschool children and its association with brain development and milk consumption

Protecting the quality of children growth and development becomes a supreme qualification for the betterment of a nation. Double burden child malnutrition is emerging worldwide which might have a strong influence to the quality of child brain development and could not be paid-off on later life. Milk places a notable portion during the infancy and childhood. Thus, the deep insight on milk consumption pattern might explain the phenomenon of double burden child malnutrition correlated to the cognitive impairments. Objective: Current study is intended (1) to examine the current face of Indonesian double burden child malnutrition: a case study in Bogor, West Java, Indonesia, (2) to investigate the association of this phenomenon with child brain development, and (3) to examine the contribution of socioeconomic status and milk consumption on this phenomenon so that able to formulate some possible solutions to encounter this problem. Design: A cross-sectional study using a structured coded questionnaire was conducted among 387 children age 5-6 years old and their parents from 8 areas in Bogor, West-Java, Indonesia on November 2012 to December 2013, to record some socioeconomic status, anthropometric measurements, and history of breast feeding. Diet and probability of milk intake was assessed by two 24 h dietary recalls and food frequency questionnaire (FFQ). Usual daily milk intake was calculated using Multiple Source Method (MSM). Some brain development indicators (IQ, EQ, learning, and memory ability) using Projective Multi-phase Orientation method was also executed to learn the correlation between double burden child malnutrition and some brain development indicator. Results and conclusions: A small picture of child double burden malnutrition is shown in Bogor, West Java, Indonesia, where prevalence of Severe Acute Malnutrition (SAM) is 27.1%, Moderate Acute Malnutrition (MAM) is 24.9%, and overnutrition is 7.7%. This phenomenon proves to impair the child brain development. The malnourished children, both under- and over- nourished children have significantly (P-value<0.05) lower memory ability compared to the normal children (memory score, N; SAM = 45.2, 60; MAM = 48.5, 61; overweight = 48.4, 43; obesity = 47.9, 60; normal = 52.4, 163). The plausible reasons behind these evidences are the lack of nutrient intake during the sprout growth period on undernourished children or increasing adiposity on overnourished children might influence the growth of hippocampus area which responsible to the memory ability. Either undernutrition or overnutrition, the preventive action on this problem is preferable to avoid ongoing cognitive performance loss of the next generation. Some possible solutions for this phenomenon are promoting breast feeding initiation and exclusive breast feeding practices for infants, supporting the consumption of a normal portion of milk (250 to 500 ml per day) for children, and breaking the chain of poverty by socioeconomic improvement. And, the national food security becomes the fundamental point for the betterment of the next. In the global context, the causes of under- and over- nutrition have to be opposed through integrated and systemic approaches for a better quality of the next generation of human beings.

Optimal Unsupervised Learning in Feedforward Neural Networks

We investigate the properties of feedforward neural networks trained with Hebbian learning algorithms. A new unsupervised algorithm is proposed which produces statistically uncorrelated outputs. The algorithm causes the weights of the network to converge to the eigenvectors of the input correlation with largest eigenvalues. The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. Applications of the algorithm to texture processing, image coding, and stereo depth edge detection are given. We show that the algorithm can lead to the development of filters qualitatively similar to those found in primate visual cortex.

Embodiment and Manipulation Learning Process for a Humanoid Hand

Babies are born with simple manipulation capabilities such as reflexes to perceived stimuli. Initial discoveries by babies are accidental until they become coordinated and curious enough to actively investigate their surroundings. This thesis explores the development of such primitive learning systems using an embodied light-weight hand with three fingers and a thumb. It is self-contained having four motors and 36 exteroceptor and proprioceptor sensors controlled by an on-palm microcontroller. Primitive manipulation is learned from sensory inputs using competitive learning, back-propagation algorithm and reinforcement learning strategies. This hand will be used for a humanoid being developed at the MIT Artificial Intelligence Laboratory.

Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

Guia per a l'adaptació a l'espai europeu d'educació superior: 4. Activitats d'aprenentatge = Guía para la adaptación al espacio europeo de educación superior: 4. Actividades de aprendizaje = Guide to Adaptation to the European Higher Education Area: 4. Learning Activities

Aquest quadern és el quart lliurement de la Guia per a l'adaptació a l'espai europeo superior. Té l'origen en el debat de la Comissió de Seguiment del Pla Pilot d'adaptació a l'Espai Europeu d'Educació Superior de la UdG i del grup de treball que s'ha constituït l'estiu del 2006 expressament per tractar el tema de les activitats d'aprenentatge

Gestión de la actividad no presencial y autoevaluación de competencias = Management of student learning outside the classroom and the self-evaluation of competences

La asignatura troncal “Evaluación Psicológica” de los estudios de Psicología y del estudio de grado “Desarrollo humano en la sociedad de la información” de la Universidad de Girona consta de 12 créditos según la Ley Orgánica de Universidades. Hasta el año académico 2004-05 el trabajo no presencial del alumno consistía en la realización de una evaluación psicológica que se entregaba por escrito a final de curso y de la cual el estudiante obtenía una calificación y revisión si se solicitaba. En el camino hacia el Espacio Europeo de Educación Superior, esta asignatura consta de 9 créditos que equivalen a un total de 255 horas de trabajo presencial y no presencial del estudiante. En los años académicos 2005-06 y 2006-07 se ha creado una guía de trabajo para la gestión de la actividad no presencial con el objetivo de alcanzar aprendizajes a nivel de aplicación y solución de problemas/pensamiento crítico (Bloom, 1975) siguiendo las recomendaciones de la Agencia para la Calidad del Sistema Universitario de Cataluña (2005). La guía incorpora: los objetivos de aprendizaje, los criterios de evaluación, la descripción de las actividades, el cronograma semanal de trabajos para todo el curso, la especificación de las tutorías programadas para la revisión de los diversos pasos del proceso de evaluación psicológica y el uso del foro para el conocimiento, análisis y crítica constructiva de las evaluaciones realizadas por los compañeros

The UK Professional Standards Framework for teaching and supporting learning in higher education

The UK Professional Standards Framework (UK PSF) for teaching and supporting learning, launched in February 2006, is a flexible framework which uses a descriptor-based approach to professional standards. There are three standard descriptors each of which is applicable to a number of staff roles and to different career stages of those engaged in teaching and supporting learning. The standard descriptors are underpinned by areas of professional activity, core knowledge and professional values. The framework provides a reference point for institutions and individuals as well as supporting ongoing development within any one standard descriptor.

Picking your profile: an academic guide to learning styles

This guide provides a brief overview on different learning styles, along with the best way to approach study for each type.

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.